




from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__		     =     logging.get_logger(__name__)

a__		     =     {
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}


class    SCREAMING_SNAKE_CASE_				(       __lowercase   ):



	"""simple docstring"""


	__magic_name__   :       Union[str, Any]      	=		'efficientformer'






	def __init__(					self		:						Union[str, Any]					,     lowerCAmelCase		:						List[int] = [3, 2, 6, 4]					,     lowerCAmelCase		:						List[int] = [48, 96, 224, 448]					,     lowerCAmelCase		:						List[bool] = [True, True, True, True]					,     lowerCAmelCase		:						int = 448					,     lowerCAmelCase		:						int = 32					,     lowerCAmelCase		:						int = 4					,     lowerCAmelCase		:						int = 7					,     lowerCAmelCase		:						int = 5					,     lowerCAmelCase		:						int = 8					,     lowerCAmelCase		:						int = 4					,     lowerCAmelCase		:						float = 0.0					,     lowerCAmelCase		:						int = 16					,     lowerCAmelCase		:						int = 3					,     lowerCAmelCase		:						int = 3					,     lowerCAmelCase		:						int = 3					,     lowerCAmelCase		:						int = 2					,     lowerCAmelCase		:						int = 1					,     lowerCAmelCase		:						float = 0.0					,     lowerCAmelCase		:						int = 1					,     lowerCAmelCase		:						bool = True					,     lowerCAmelCase		:						bool = True					,     lowerCAmelCase		:						float = 1E-5					,     lowerCAmelCase		:						str = "gelu"					,     lowerCAmelCase		:						float = 0.02					,     lowerCAmelCase		:						float = 1E-12					,     lowerCAmelCase		:						int = 224					,     lowerCAmelCase		:						float = 1E-05					,     **lowerCAmelCase		:						str					,     )	->       None:







						"""simple docstring"""







						super().__init__(**lowerCAmelCase	)

						__UpperCamelCase					:	Optional[Any]             =      hidden_act
						__UpperCamelCase					:	Any             =      hidden_dropout_prob
						__UpperCamelCase					:	str             =      hidden_sizes
						__UpperCamelCase					:	Dict             =      num_hidden_layers
						__UpperCamelCase					:	Optional[Any]             =      num_attention_heads
						__UpperCamelCase					:	str             =      initializer_range
						__UpperCamelCase					:	List[str]             =      layer_norm_eps
						__UpperCamelCase					:	str             =      patch_size
						__UpperCamelCase					:	str             =      num_channels
						__UpperCamelCase					:	Any             =      depths
						__UpperCamelCase					:	Tuple             =      mlp_expansion_ratio
						__UpperCamelCase					:	List[str]             =      downsamples
						__UpperCamelCase					:	Optional[int]             =      dim
						__UpperCamelCase					:	Dict             =      key_dim
						__UpperCamelCase					:	Optional[Any]             =      attention_ratio
						__UpperCamelCase					:	Dict             =      resolution
						__UpperCamelCase					:	Union[str, Any]             =      pool_size
						__UpperCamelCase					:	Tuple             =      downsample_patch_size
						__UpperCamelCase					:	Optional[int]             =      downsample_stride
						__UpperCamelCase					:	Optional[int]             =      downsample_pad
						__UpperCamelCase					:	Union[str, Any]             =      drop_path_rate
						__UpperCamelCase					:	Union[str, Any]             =      num_metaad_blocks
						__UpperCamelCase					:	List[str]             =      distillation
						__UpperCamelCase					:	str             =      use_layer_scale
						__UpperCamelCase					:	Tuple             =      layer_scale_init_value
						__UpperCamelCase					:	str             =      image_size
						__UpperCamelCase					:	int             =      batch_norm_eps








import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase    :   List[str]						=			get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	ReformerTokenizer
	_A								=	ReformerTokenizerFast
	_A								=	True
	_A								=	False
	_A								=	True







	def   _lowerCamelCase							(   self						:Dict		)						->					Union[str, Any]:
								super().setUp()

								__UpperCamelCase      :  List[str]       =   ReformerTokenizer(a	,  keep_accents=a		)
								tokenizer.save_pretrained(self.tmpdirname		)







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "<s>"
								__UpperCamelCase      :  Dict       =   1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(a		)	,  a		)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(a		)	,  a		)







	def   _lowerCamelCase							(   self						:Dict		)						->					List[Any]:
								__UpperCamelCase      :  int       =   list(self.get_tokenizer().get_vocab().keys()		)

								self.assertEqual(vocab_keys[0]	,  "<unk>"		)
								self.assertEqual(vocab_keys[1]	,  "<s>"		)
								self.assertEqual(vocab_keys[-1]	,  "j"		)
								self.assertEqual(len(a		)	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					str:
								self.assertEqual(self.get_tokenizer().vocab_size	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					List[str]:
								if not self.test_rust_tokenizer:
															return

								__UpperCamelCase      :  Union[str, Any]       =   self.get_tokenizer()
								__UpperCamelCase      :  List[str]       =   self.get_rust_tokenizer()

								__UpperCamelCase      :  Union[str, Any]       =   "I was born in 92000, and this is falsé."

								__UpperCamelCase      :  List[str]       =   tokenizer.tokenize(a		)
								__UpperCamelCase      :  int       =   rust_tokenizer.tokenize(a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  List[str]       =   tokenizer.encode(a	,  add_special_tokens=a		)
								__UpperCamelCase      :  Tuple       =   rust_tokenizer.encode(a	,  add_special_tokens=a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  Union[str, Any]       =   self.get_rust_tokenizer()
								__UpperCamelCase      :  int       =   tokenizer.encode(a		)
								__UpperCamelCase      :  List[Any]       =   rust_tokenizer.encode(a		)
								self.assertListEqual(a	,  a		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:int=1_5		)						->					Any:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
																						__UpperCamelCase      :  List[Any]       =   self.rust_tokenizer_class.from_pretrained(a	,  **a		)

																						# Simple input
																						__UpperCamelCase      :  Union[str, Any]       =   "This is a simple input"
																						__UpperCamelCase      :  Dict       =   ["This is a simple input 1", "This is a simple input 2"]
																						__UpperCamelCase      :  Tuple       =   ("This is a simple input", "This is a pair")
																						__UpperCamelCase      :  Optional[Any]       =   [
																						    ("This is a simple input 1", "This is a simple input 2"),
																						    ("This is a simple pair 1", "This is a simple pair 2"),
																						]

																						# Simple input tests
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					str:
								pass







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Optional[Any]:
								__UpperCamelCase      :  List[Any]       =   ReformerTokenizer(a	,  keep_accents=a		)

								__UpperCamelCase      :  Tuple       =   tokenizer.tokenize("This is a test"		)
								self.assertListEqual(a	,  ["▁This", "▁is", "▁a", "▁t", "est"]		)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(a		)	,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]	,  )

								__UpperCamelCase      :  str       =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "9",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "é",
								        ".",
								    ]	,  )
								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_tokens_to_ids(a		)
								self.assertListEqual(
								    a	,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]	,  )

								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_ids_to_tokens(a		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "<unk>",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "<unk>",
								        ".",
								    ]	,  )







	@cached_property
	def   _lowerCamelCase							(   self						:Any		)						->					List[str]:
								return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "Hello World!"
								__UpperCamelCase      :  Union[str, Any]       =   [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					List[str]:
								__UpperCamelCase      :  Dict       =   (
								    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
								    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
								)
								__UpperCamelCase      :  List[Any]       =   [
								    1_0_8,
								    2_6_5,
								    2_4,
								    1_1_1,
								    4,
								    2_5_8,
								    1_5_6,
								    3_5,
								    2_8,
								    2_7_5,
								    3,
								    2_5_9,
								    2_9_7,
								    2_6_0,
								    8_4,
								    4,
								    3_5,
								    1_1_0,
								    4_4,
								    8,
								    2_5_9,
								    9_1,
								    2_6_8,
								    2_1,
								    1_1,
								    2_0_9,
								    2_7_4,
								    1_0_9,
								    2_6_6,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    3_1_5,
								    2_5_8,
								    2_7_8,
								    2_5_8,
								    2_7_7,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_8,
								    2_5_8,
								    3_1_9,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_7,
								    2_5_8,
								    3_1_5,
								    2_5_8,
								    2_8_9,
								    2_5_8,
								    2_7_8,
								    9_9,
								    2_6_9,
								    2_6_6,
								    2_6_2,
								    8,
								    2_5_9,
								    2_4_1,
								    4,
								    2_1_7,
								    2_3_0,
								    2_6_8,
								    2_6_6,
								    5_5,
								    1_6_8,
								    1_0_6,
								    7_5,
								    1_9_3,
								    2_6_6,
								    2_2_3,
								    2_7,
								    4_9,
								    2_6,
								    2_8_2,
								    2_5,
								    2_6_4,
								    2_9_9,
								    1_9,
								    2_6,
								    0,
								    2_5_8,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    1_7_6,
								    1_8_3,
								    2_7_0,
								    1_1,
								    2_6_2,
								    4_2,
								    6_1,
								    2_6_5,
								]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@require_torch
	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								__UpperCamelCase      :  Tuple       =   list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
								__UpperCamelCase      :  Union[str, Any]       =   " ".join(a		)
								__UpperCamelCase      :  Tuple       =   self.big_tokenizer.encode_plus(a	,  return_tensors="pt"		)
								__UpperCamelCase      :  Optional[int]       =   self.big_tokenizer.batch_encode_plus([sequence, sequence]	,  return_tensors="pt"		)

								__UpperCamelCase      :  Optional[Any]       =   ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								__UpperCamelCase      :  Tuple       =   encoded_sequence["input_ids"].shape
								__UpperCamelCase      :  int       =   ReformerModel(a		)

								# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
															model(**a		)
															model(**a		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Any:
								# fmt: off
								__UpperCamelCase      :  Any       =   {"input_ids": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# This tokenizer does not know some characters like ")".
								# That is the reason why we use very simple texts here.
								# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
								__UpperCamelCase      :  List[str]       =   [
								    "This is a very simple sentence.",
								    "The quick brown fox jumps over the lazy dog.",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=a	,  model_name="google/reformer-crime-and-punishment"	,  revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"	,  padding=a	,  sequences=a	,  )